Deploying intelligent service and executing inference tasks in the proximity of the edge enable models to access enormous real-time data generated by the edge devices. However, the dilemma of fulfilling service demands with limited resources at edge devices impairs the efficacy of conventional data-oriented communication systems. To achieve a better trade-off between inference accuracy and communication overhead, in this paper, we propose a dynamic unmanned aerial vehicle (UAV)-assisted cooperative edge inference system, where a UAV acts as an edge server to aggregate the wide-view features from mobile sensors through Over-the-Air computation (AirComp) to complete the inference task cooperatively. Discriminant gain, an effective indicator for the inference accuracy, is adopted to realize task-oriented design. To exploit channel diversity and data diversity in the multi-device cooperative edge inference system, we maximize the discriminant gain of the AirComp feature aggregation by jointly optimizing the UAV trajectory and the power allocation policy with respect to the different important levels of feature dimensions. An alternating algorithm and a successive convex approximation (SCA)-based method are then proposed to solve the optimization problem. Numerical simulations further validate the efficacy of the proposed design compared to the baselines.